TECHNOLOGICAL FIELD
[0001] An example embodiment of the present invention relates to determining lane-level
route guidance, and more particularly, to establishing recommended lane-level guidance
between an origin and a destination based on a safe and efficient path.
BACKGROUND
[0002] Maps have been used for centuries for providing route geometry and geographical information.
Conventional paper maps including static images of roadways and geographic features
from a snapshot in history have given way to digital maps presented on computers and
mobile devices. These digital maps can be updated and revised such that users have
the most-current maps available to them each time they view a map hosted by a mapping
service server. Digital maps can further be enhanced with dynamic information, such
as traffic information in real time along roads and through intersections.
[0003] Traffic data that is provided on digital maps is generally based on crowd-sourced
data from mobile devices or probe data. The traffic data is typically reflective of
a collective group of mobile devices traveling along a road segment, and may be useful
in vehicle navigation applications in order for a user to avoid heavy traffic routes
between an origin and a destination. However, the specificity with which route guidance
is provided is generally limited.
BRIEF SUMMARY
[0004] A method, apparatus, and computer program product are provided in accordance with
an example embodiment for determining lane-level route guidance, and more particularly,
to establishing recommended lane-level guidance between an origin and a destination
based on a safe, efficient, or popular path. Embodiments may provide a mapping system
including a memory having map data stored therein and processing circuitry. The processing
circuitry may be configured to: receive a plurality of probe data points, each probe
data point received from a probe apparatus of a plurality of probe apparatuses, each
probe apparatus traveling between a respective origin and a respective destination,
each probe apparatus including one or more sensors and being onboard a respective
vehicle, where each probe data point includes location information associated with
the respective probe apparatus; determine a lane-level maneuver pattern for each probe
apparatus between the origin and the destination; provide for storage of the lane-level
maneuver patterns for each probe apparatus in the memory; group together lane-level
maneuver patterns for probe apparatuses having an origin and destination pair within
a predefined similarity of origin and destination pairs of other probe apparatuses;
generate a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group;
provide for route guidance to a vehicle based on the generated lane-level maneuver
pattern.
[0005] According to some embodiments, the route guidance provided to a vehicle may include
lane-level route guidance for an autonomous vehicle along a route between an origin
and a destination. The processing circuitry configured to determine a lane-level maneuver
pattern for each probe apparatus between the origin and the destination may include
processing circuitry configured to: map match probe data from a respective probe apparatus
to one or more road segments along a route between the origin and the destination;
map match probe data from the respective probe apparatus to individual lanes of the
one or more road segments along the route between the origin and the destination;
and determine a lane-level maneuver pattern for the respective probe apparatus based
on map matched lanes of the one or more road segments in a sequence from the origin
to the destination. The processing circuitry configured to generate a lane-level maneuver
pattern for each group based on at least one of a popularity, efficiency, or relatively
safe lane-level maneuver pattern for the respective group may include processing circuitry
configured to: apply a clustering algorithm to each group to obtain clusters of lane-level
maneuver patterns within the respective group; select the cluster having a largest
number of associated probe apparatuses; and generate the lane-level maneuver pattern
for the respective group based on the lane-level maneuver pattern of the cluster having
the largest number of associated probe apparatuses.
[0006] The processing circuitry configured to generate a lane-level maneuver pattern for
each group based on at least one of a popularity, efficiency, or relatively safe lane-level
maneuver pattern for the respective group may include processing circuitry configured
to: apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group; select the cluster having a shortest time difference
between the origin and the destination; and generate the lane-level maneuver pattern
for the respective group based on the lane-level maneuver pattern of the cluster having
the shortest time difference betwen the origin and the destination.
[0007] The processing circuitry of mapping systems of example embodiments configured to
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses may include processing circuitry configured to: group together
lane-level maneuver patterns for probe apparatuses having an origin and destination
pair within a predefined similarity of origin and destination pairs of other probe
apparatuses and having traveled between the origin and the destination within a predefined
epoch, where the processing circuitry configured to generate a lane-level maneuver
pattern for each group based on at least one of a popularity, efficiency, or relatively
safe lane-level maneuver pattern for the respective group may further include processing
circuitry configured to generate a lane-level maneuver pattern based, at least in
part, on an epoch in which the route between the origin and the destination will be
traveled. The processing circuitry may optionally be configured to provide driving
speed, acceleration, and deceleration on a per-lane basis of the lane-level maneuver
pattern based, at least in part, on the group of probe apparatuses associated with
the lane-level maneuver pattern.
[0008] Embodiments described herein may provide an apparatus including at least one processor
and at least one memory including computer program code. The at least one memory and
computer program code configured to, with the processor, cause the apparatus to: receive
a plurality of probe data points, each probe data point received from a probe apparatus
of a plurality of probe apparatuses, each probe apparatus traveling between a respective
origin and destination pair, each probe apparatus including one or more sensors and
being onboard a respective vehicle, where each probe data point includes location
information associated with the respective probe apparatus; determine a lane-level
maneuver pattern for each probe apparatus between the origin and the destination;
provide for storage of the lane-level maneuver patterns for each probe apparatus in
the memory; group together lane-level maneuver patterns for probe apparatuses having
an origin and destination pair within a predefined similarity of origin and destination
pairs of other probe apparatuses; generate a lane-level maneuver pattern for each
group based on at least one of a popularity, efficiency, or relatively safe lane-level
maneuver pattern for the respective group; and provide for route guidance to a vehicle
based on the generated lane-level maneuver pattern. The route guidance provided to
a vehicle may include lane-level route guidance for an autonomous vehicle along a
route between an origin and a destination.
[0009] According to some embodiments, causing the apparatus to determine a lane-level maneuver
pattern for each probe apparatus between the origin and the destination may include
causing the apparatus to: map match probe data from a respective probe apparatus to
one or more road segments along a route between the origin and the destination; map
match probe data from the respective probe apparatus to individual lanes of the one
or more road segments along the route between the origin and the destination; and
determine a lane-level maneuver pattern for the respective probe apparatus based on
map matched lanes of the one or more road segments in a sequence from the origin to
the destination. Causing the apparatus to generate a lane-level maneuver pattern for
each group based on at least one of a popularity, efficiency, or relatively safe lane-level
maneuver pattern for the respective group may include causing the apparatus to: apply
a clustering algorithm to each group to obtain clusters of lane-level maneuver patterns
within the respective group; select the cluster having a largest number of associated
probe apparatuses; and generate the lane-level maneuver pattern for the respective
group based on the lane-level maneuver pattern of the cluster having the largest number
of associated probe apparatuses.
[0010] Causing the apparatus to group together lane-level maneuver patterns for probe apparatuses
having an origin and destination pair within a predefined similarity of origin and
destination pairs of other probe apparatuses may include causing the apparatus to:
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses and having traveled between the origin and the destination
within a predetermined epoch; where causing the apparatus to generate a lane-level
maneuver pattern for each group based on at least one of a popularity, efficiency,
or relatively safe lane-level maneuver pattern for the respective group may include
causing the apparatus to generate a lane-level maneuver pattern based, at least in
part, on an epoch in which the route between the origin and the destination will be
traveled. The apparatus of example embodiments may be caused to provide driving speed,
acceleration, and deceleration on a per-lane basis of the lane-level maneuver pattern
based, at least in part, on the group of probe apparatuses associated with the lane-level
maneuver pattern.
[0011] Embodiments described herein may provide a method including: receiving a plurality
of probe data points, each probe data point received from a probe apparatus of a plurality
of probe apparatuses, each probe apparatus traveling between a respective origin and
destination pair, each probe apparatus including one or more sensors and being onboard
a respective vehicle, where each probe data point includes location information associated
with the respective probe apparatus; determining a lane-level maneuver pattern for
each probe apparatus between the origin and the destination; providing for storage
of the lane-level maneuver patterns for each probe apparatus in the memory; grouping
together lane-level maneuver patterns for probe apparatuses having an origin and destination
pair within a predefined similarity of origin and destination pairs of other apparatuses;
generating a lane-level maneuver pattern for each group based on at least one of a
popularity, efficiency, or relatively safe lane-level maneuver pattern for the respective
group; and providing for route guidance to a vehicle based on the generated lane-level
maneuver pattern. The route guidance may include lane-level route guidance for an
autonomous vehicle along a route between an origin and a destination.
[0012] According to some methods, determining a lane-level maneuver pattern for each probe
apparatus between the origin and the destination may include: map matching probe data
from a respective probe apparatus to one or more road segments along a route between
the origin and the destination; map matching probe data from the respective probe
apparatus to individual lanes of the one or more road segments along the route between
the origin and the destination; and determining a lane-level maneuver pattern for
the respective probe apparatus based on map matched lanes of the one or more road
segments in a sequence from the origin to the destination. Generating a lane-level
maneuver pattern for each group based on at least one of a popularity, efficiency,
or relatively safe lane-level maneuver pattern for the respective group may include:
applying a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group; selecting the cluster having a largest number
of associated probe apparatuses; and generating the lane-level maneuver pattern for
the respective group based on the lane-level maneuver pattern of the cluster having
the largest number of associated probe apparatuses.
[0013] Methods including generating a lane-level maneuver pattern for each group based on
at least one of a popularity, efficiency, or relatively safe lane-level maneuver pattern
for the respective group may include: applying a clustering algorithm to each group
to obtain clusters of lane-level maneuver patterns within the respective group; selecting
the cluster having a shortest time difference between the origin and the destination;
and generating the lane-level maneuver pattern for the respective group based on the
lane-level maneuver pattern of the cluster having the shortest time difference betwen
the origin and the destination. Grouping together lane-level maneuver patterns for
probe apparatuses having an origin and destination pair within a predefined similarity
of origin and destination pairs of other probe apparatuses may include: grouping together
lane-level maneuver patterns for probe apparatuses having an origin and destination
pair within a predefined similarity of origin and destination pairs of other probe
apparatuses and having traveled between the origin and the destination within a predetermined
epoch; where generating a lane-level maneuver pattern for each group based on at least
one of a popularity, efficiency, or relatively safe lane-level maneuver pattern for
the respective group may further include generating a lane-level maneuver pattern
based, at least in part, on an epoch in which the route between the origin and the
destination will be traveled.
[0014] Embodiments described herein may provide an apparatus including: means for receiving
a plurality of probe data points, each probe data point received from a probe apparatus
of a plurality of probe apparatuses, each probe apparatus traveling between a respective
origin and destination pair, each probe apparatus including one or more sensors and
being onboard a respective vehicle, where each probe data point includes location
information associated with the respective probe apparatus; means for determining
a lane-level maneuver pattern for each probe apparatus between the origin and the
destination; means for providing for storage of the lane-level maneuver patterns for
each probe apparatus in the memory; means for grouping together lane-level maneuver
patterns for probe apparatuses having an origin and destination pair within a predefined
similarity of origin and destination pairs of other apparatuses; means for generating
a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group;
and means for providing for route guidance to a vehicle based on the generated lane-level
maneuver pattern. The route guidance may include lane-level route guidance for an
autonomous vehicle along a route between an origin and a destination.
[0015] According to some embodiments, the means for determining a lane-level maneuver pattern
for each probe apparatus between the origin and the destination may include: means
for map matching probe data from a respective probe apparatus to one or more road
segments along a route between the origin and the destination; means for map matching
probe data from the respective probe apparatus to individual lanes of the one or more
road segments along the route between the origin and the destination; and means for
determining a lane-level maneuver pattern for the respective probe apparatus based
on map matched lanes of the one or more road segments in a sequence from the origin
to the destination. The means for generating a lane-level maneuver pattern for each
group based on at least one of a popularity, efficiency, or relatively safe lane-level
maneuver pattern for the respective group may include: means for applying a clustering
algorithm to each group to obtain clusters of lane-level maneuver patterns within
the respective group; selecting the cluster having a largest number of associated
probe apparatuses; and means for generating the lane-level maneuver pattern for the
respective group based on the lane-level maneuver pattern of the cluster having the
largest number of associated probe apparatuses.
[0016] An apparatus including means for generating a lane-level maneuver pattern for each
group based on at least one of a popularity, efficiency, or relatively safe lane-level
maneuver pattern for the respective group may include: means for applying a clustering
algorithm to each group to obtain clusters of lane-level maneuver patterns within
the respective group; means for selecting the cluster having a shortest time difference
between the origin and the destination; and means for generating the lane-level maneuver
pattern for the respective group based on the lane-level maneuver pattern of the cluster
having the shortest time difference betwen the origin and the destination. The means
for grouping together lane-level maneuver patterns for probe apparatuses having an
origin and destination pair within a predefined similarity of origin and destination
pairs of other probe apparatuses may include: means for grouping together lane-level
maneuver patterns for probe apparatuses having an origin and destination pair within
a predefined similarity of origin and destination pairs of other probe apparatuses
and having traveled between the origin and the destination within a predetermined
epoch; where the means for generating a lane-level maneuver pattern for each group
based on at least one of a popularity, efficiency, or relatively safe lane-level maneuver
pattern for the respective group may further include means for generating a lane-level
maneuver pattern based, at least in part, on an epoch in which the route between the
origin and the destination will be traveled.
[0017] The following numbered paragraphs are also disclosed:
- 1. A mapping system comprising:
a memory comprising map data; and
processing circuitry configured to:
receive a plurality of probe data points, each probe data point received from a probe
apparatus of a plurality of probe apparatuses, each probe apparatus traveling between
a respective origin and destination pair, each probe apparatus comprising one or more
sensors and being onboard a respective vehicle, wherein each probe data point comprises
location information associated with the respective probe apparatus;
determine a lane-level maneuver pattern for each probe apparatus between the origin
and the destination;
provide for storage of the lane-level maneuver patterns for each probe apparatus in
the memory;
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses;
generate a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group;
and
provide for route guidance to a vehicle based on the generated lane-level maneuver
pattern.
- 2. The mapping system of paragraph 1, wherein the route guidance provided to a vehicle
comprises lane-level route guidance for an autonomous vehicle along a route between
an origin and a destination.
- 3. The mapping system of paragraph 1, wherein the processing circuitry configured
to determine a lane-level maneuver pattern for each probe apparatus between the origin
and the destination comprises processing circuitry configured to:
map match probe data from a respective probe apparatus to one or more road segments
along a route between the origin and the destination;
map match probe data from the respective probe apparatus to individual lanes of the
one or more road segments along the route between the origin and the destination;
and
determine a lane-level maneuver pattern for the respective probe apparatus based on
map matched lanes of the one or more road segments in a sequence from the origin to
the destination.
- 4. The mapping system of paragraph 1, wherein the processing circuitry configured
to generate a lane-level maneuver pattern for each group based on at least one of
a popularity, efficiency, or relatively safe lane-level maneuver pattern for the respective
group comprises processing circuitry configured to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a largest number of associated probe apparatuses; and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the largest number of associated probe apparatuses.
- 5. The mapping system of paragraph 1, wherein the processing circuitry configured
to generate a lane-level maneuver pattern for each group based on at least one of
a popularity, efficiency, or relatively safe lane-level maneuver pattern for the respective
group comprises processing circuitry configured to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a shortest time difference between the origin and the destination;
and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the shortest time difference between the origin
and the destination
- 6. The mapping system of paragraph 1, wherein the processing circuitry configured
to group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses further comprises processing circuitry configured to:
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses and having traveled between the origin and the destination
within a predetermined epoch;
wherein the processing circuitry configured to generate a lane-level maneuver pattern
for each group based on at least one of a popularity, efficiency, or relatively safe
lane-level maneuver pattern for the respective group further comprises processing
circuitry to generate a lane-level maneuver pattern based, at least in part, on an
epoch in which the route between the origin and the destination will be traveled.
- 7. The mapping system of paragraph 1, wherein the processing circuitry is further
configured to:
provide driving speed, acceleration, and deceleration on a per-lane basis of the lane-level
maneuver pattern based, at least in part, on the group of probe apparatuses associated
with the lane-level maneuver pattern.
- 8. An apparatus comprising at least one processor and at least one memory including
computer program code, the at least one memory and computer program code configured
to, with the processor, cause the apparatus to at least:
receive a plurality of probe data points, each probe data point received from a probe
apparatus of a plurality of probe apparatuses, each probe apparatus traveling between
a respective origin and destination pair, each probe apparatus comprising one or more
sensors and being onboard a respective vehicle, wherein each probe data point comprises
location information associated with the respective probe apparatus;
determine a lane-level maneuver pattern for each probe apparatus between the origin
and the destination;
provide for storage of the lane-level maneuver patterns for each probe apparatus in
the memory;
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses;
generate a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group;
and
provide for route guidance to a vehicle based on the generated lane-level maneuver
pattern.
- 9. The apparatus of paragraph 8, wherein the route guidance provided to a vehicle
comprises lane-level route guidance for an autonomous vehicle along a route between
an origin and a destination.
- 10. The apparatus of paragraph 8, wherein causing the apparatus to determine a lane-level
maneuver pattern for each probe apparatus between the origin and the destination comprises
causing the apparatus to:
map match probe data from a respective probe apparatus to one or more road segments
along a route between the origin and the destination;
map match probe data from the respective probe apparatus to individual lanes of the
one or more road segments along the route between the origin and the destination;
and
determine a lane-level maneuver pattern for the respective probe apparatus based on
map matched lanes of the one or more road segments in a sequence from the origin to
the destination.
- 11. The apparatus of paragraph 8, wherein causing the apparatus to generate a lane-level
maneuver pattern for each group based on at least one of a popularity, efficiency,
or relatively safe lane-level maneuver pattern for the respective group comprises
causing the apparatus to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a largest number of associated probe apparatuses; and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the largest number of associated probe apparatuses.
- 12. The apparatus of paragraph 8, wherein causing the apparatus to generate a lane-level
maneuver pattern for each group based on at least one of a popularity, efficiency,
or relatively safe lane-level maneuver pattern for the respective group comprises
causing the apparatus to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a shortest time difference between the origin and the destination;
and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the shortest time difference between the origin
and the destination.
- 13. The apparatus of paragraph 8, wherein causing the apparatus to group together
lane-level maneuver patterns for probe apparatuses having an origin and destination
pair within a predefined similarity of origin and destination pairs of other probe
apparatuses further comprises causing the apparatus to:
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses and having traveled between the origin and the destination
within a predetermined epoch;
wherein causing the apparatus to generate a lane-level maneuver pattern for each group
based on at least one of a popularity, efficiency, or relatively safe lane-level maneuver
pattern for the respective group further comprises causing the apparatus to generate
a lane-level maneuver pattern based, at least in part, on an epoch in which the route
between the origin and the destination will be traveled.
- 14. The apparatus of paragraph 8, wherein the apparatus is further caused to:
provide driving speed, acceleration, and deceleration on a per-lane basis of the lane-level
maneuver pattern based, at least in part, on the group of probe apparatuses associated
with the lane-level maneuver pattern.
- 15. A method comprising:
receiving a plurality of probe data points, each probe data point received from a
probe apparatus of a plurality of probe apparatuses, each probe apparatus traveling
between a respective origin and destination pair, each probe apparatus comprising
one or more sensors and being onboard a respective vehicle, wherein each probe data
point comprises location information associated with the respective probe apparatus;
determining a lane-level maneuver pattern for each probe apparatus between the origin
and the destination;
providing for storage of the lane-level maneuver patterns for each probe apparatus
in a memory;
grouping together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses;
generating a lane-level maneuver pattern for each group based on at least one of a
popularity, efficiency, or relatively safe lane-level maneuver pattern for the respective
group; and
providing for route guidance to a vehicle based on the generated lane-level maneuver
pattern.
- 16. The method of paragraph 15, wherein the route guidance provided to a vehicle comprises
lane-level route guidance for an autonomous vehicle along a route between an origin
and a destination.
- 17. The method of paragraph 15, wherein determining a lane-level maneuver pattern
for each probe apparatus between the origin and the destination comprises:
map matching probe data from a respective probe apparatus to one or more road segments
along a route between the origin and the destination;
map matching probe data from the respective probe apparatus to individual lanes of
the one or more road segments along the route between the origin and the destination;
and
determining a lane-level maneuver pattern for the respective probe apparatus based
on map matched lanes of the one or more road segments in a sequence from the origin
to the destination.
- 18. The method of paragraph 15, wherein generating a lane-level maneuver pattern for
each group based on at least one of a popularity, efficiency, or relatively safe lane-level
maneuver pattern for the respective group comprises:
applying a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
selecting the cluster having a largest number of associated probe apparatuses; and
generating the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the largest number of associated probe apparatuses.
- 19. The method of paragraph 15, wherein generating a lane-level maneuver pattern for
each group based on at least one of a popularity, efficiency, or relatively safe lane-level
maneuver pattern for the respective group comprises:
applying a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
selecting the cluster having a shortest time difference between the origin and the
destination; and
generating the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the shortest time difference between the origin
and the destination.
- 20. The method of paragraph 15, wherein grouping together lane-level maneuver patterns
for probe apparatuses having an origin and destination pair within a predefined similarity
of origin and destination pairs of other probe apparatuses further comprises:
grouping together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses and having traveled between the origin and the destination
within a predetermined epoch;
wherein generating a lane-level maneuver pattern for each group based on at least
one of a popularity, efficiency, or relatively safe lane-level maneuver pattern for
the respective group further comprises generating a lane-level maneuver pattern based,
at least in part, on an epoch in which the route between the origin and the destination
will be traveled.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Having thus described example embodiments of the invention in general terms, reference
will now be made to the accompanying drawings, which are not necessarily drawn to
scale, and wherein:
Figure 1 illustrates a communications diagram in accordance with an example embodiment;
Figure 2 is a block diagram of an apparatus that may be specifically configured for
establishing lane-level route guidance between an origin and a destination based on
a safe and efficient lane-level path in accordance with an example embodiment described
herein;
Figure 3 illustrates a road segment including a plurality of lane-level paths along
the road segment according to an example embodiment described herein;
Figure 4 illustrates the road segment of Figure 3 along sub segments of the road segment
according to an example embodiment described herein; and
Figure 5 is a flowchart of a method for determining lane-level route guidance, and
more particularly, to establishing recommended lane-level guidance between an origin
and a destination based on a safe, efficient, or popular path according to an example
embodiment described herein.
DETAILED DESCRIPTION
[0019] Some embodiments of the present invention will now be described more fully hereinafter
with reference to the accompanying drawings, in which some, but not all, embodiments
of the invention are shown. Indeed, various embodiments of the invention may be embodied
in many different forms and should not be construed as limited to the embodiments
set forth herein; rather, these embodiments are provided so that this disclosure will
satisfy applicable legal requirements. Like reference numerals refer to like elements
throughout. As used herein, the terms "data," "content," "information," and similar
terms may be used interchangeably to refer to data capable of being transmitted, received
and/or stored in accordance with embodiments of the present invention. Thus, use of
any such terms should not be taken to limit the spirit and scope of embodiments of
the present invention.
[0020] A method, apparatus, and computer program product are provided herein in accordance
with an example embodiment for deriving lane-level guidance insight from historical
data to obtain a pattern that can be used to recommend a safer path to drive/navigate
a road segment. Embodiments may optionally factor in time of day and weather conditions
when recommending a lane-level path from an origin to a destination. FIG. 1 illustrates
a communication diagram of an example embodiment of a system for implementing example
embodiments described herein. The illustrated embodiment of FIG. 1 includes a map
services provider system 116, a processing server 102 in data communication with a
user equipment (UE) 104 and/or a geographic map database, e.g., map database 108 through
a network 112, and one or more mobile devices 114. The mobile device 114 may be associated,
coupled, or otherwise integrated with a vehicle, such as an advanced driver assistance
system (ADAS), for example. Additional, different, or fewer components may be provided.
For example, many mobile devices 114 may connect with the network 112. The map services
provider 116 may include computer systems and network of a system operator. The processing
server 102 may include the map database 108, such as a remote map server. The network
may be wired, wireless, or any combination of wired and wireless communication networks,
such as cellular, Wi-Fi, internet, local area networks, or the like.
[0021] The user equipment 104 may include a mobile computing device such as a laptop computer,
tablet computer, mobile phone, smart phone, navigation unit, personal data assistant,
watch, camera, or the like. Additionally or alternatively, the user equipment 104
may be a fixed computing device, such as a personal computer, computer workstation,
kiosk, office terminal computer or system, or the like. Processing server 102 may
be one or more fixed or mobile computing devices. The user equipment 104 may be configured
to access the map database 108 via the processing server 102 through, for example,
a mapping application, such that the user equipment may provide navigational assistance
to a user among other services provided through access to the map services provider
116.
[0022] The map database 108 may include node data, road segment data or link data, point
of interest (POI) data, or the like. The map database 108 may also include cartographic
data, routing data, and/or maneuvering data. According to some example embodiments,
the road segment data records may be links or segments representing roads, streets,
or paths, as may be used in calculating a route or recorded route information for
determination of one or more personalized routes. The node data may be end points
corresponding to the respective links or segments of road segment data. The road link
data and the node data may represent a road network, such as used by vehicles, cars,
trucks, buses, motorcycles, and/or other entities. Optionally, the map database 108
may contain path segment and node data records or other data that may represent pedestrian
paths or areas in addition to or instead of the vehicle road record data, for example.
The road/link segments and nodes can be associated with attributes, such as geographic
coordinates, street names, address ranges, speed limits, turn restrictions at intersections,
and other navigation related attributes, as well as POIs, such as fueling stations,
hotels, restaurants, museums, stadiums, offices, auto repair shops, buildings, stores,
parks, etc. The map database 108 can include data about the POIs and their respective
locations in the POI records. The map database 108 may include data about places,
such as cities, towns, or other communities, and other geographic features such as
bodies of water, mountain ranges, etc. Such place or feature data can be part of the
POI data or can be associated with POIs or POI data records (such as a data point
used for displaying or representing a position of a city). In addition, the map database
108 can include event data (e.g., traffic incidents, construction activities, scheduled
events, unscheduled events, etc.) also known as a context associated with the POI
data records or other records of the map database 108.
[0023] The map database 108 may be maintained by a content provider e.g., a map services
provider in association with a services platform. By way of example, the map services
provider can collect geographic data to generate and enhance the map database 108.
There can be different ways used by the map services provider to collect data. These
ways can include obtaining data from other sources, such as municipalities or respective
geographic authorities. In addition, the map services provider can employ field personnel
to travel by vehicle along roads throughout the geographic region to observe features
and/or record information about them, for example. Also, remote sensing, such as aerial
or satellite photography, can be used to generate map geometries directly or through
machine learning as described herein. Further, crowd-sourced data from vehicles traveling
along the road links in the road network may provide information relating to their
respective speed of travel, which may inform the map services provider with respect
to traffic volumes and congestion and lane-level paths traveled by the respective
vehicles. Such traffic congestion information and lane-level path information may
be used during navigation or routing operations such that a user may be provided guidance
as to which lane they should be driving along various road segments along the route
from their origin to their destination.
[0024] The map database 108 may be a master map database stored in a format that facilitates
updating, maintenance, and development. For example, the master map database or data
in the master map database can be in an Oracle spatial format or other spatial format,
such as for development or production purposes. The Oracle spatial format or development/production
database can be compiled into a delivery format, such as a geographic data files (GDF)
format. The data in the production and/or delivery formats can be compiled or further
compiled to form geographic database products or databases, which can be used in end
user navigation devices or systems.
[0025] For example, geographic data may be compiled (such as into a platform specification
format (PSF) format) to organize and/or configure the data for performing navigation-related
functions and/or services, such as route calculation, route guidance, map display,
speed calculation, distance and travel time functions, and other functions, by a navigation
device, such as by user equipment 104, for example. The navigation-related functions
can correspond to vehicle navigation or other types of navigation. While example embodiments
described herein generally relate to vehicular travel along roads, example embodiments
may be implemented for bicycle travel along bike paths, boat travel along maritime
navigational routes, aerial travel along highways in the sky, etc. The compilation
to produce the end user databases can be performed by a party or entity separate from
the map services provider. For example, a customer of the map services provider, such
as a navigation device developer or other end user device developer, can perform compilation
on a received map database in a delivery format to produce one or more compiled navigation
databases.
[0026] As mentioned above, the server side map database 108 may be a master geographic database,
but in alternate embodiments, a client side map database 108 may represent a compiled
navigation database that may be used in or with end user devices (e.g., user equipment
104) to provide navigation and/or map-related functions. For example, the map database
108 may be used with the end user device 104 to provide an end user with navigation
features. In such a case, the map database 108 can be downloaded or stored on the
end user device (user equipment 104) which can access the map database 108 through
a wireless or wired connection, such as via a processing server 102 and/or the network
112, for example.
[0027] In one embodiment, the end user device or user equipment 104 can be an in-vehicle
navigation system, such as an ADAS, a personal navigation device (PND), a portable
navigation device, a cellular telephone, a smart phone, a personal digital assistant
(PDA), a watch, a camera, a computer, and/or other device that can perform navigation-related
functions, such as digital routing and map display. An end user can use the user equipment
104 for navigation and map functions such as guidance and map display, for example,
and for determination of one or more personalized routes or route segments based on
one or more calculated and recorded routes, according to some example embodiments.
[0028] The processing server 102 may receive probe data from a mobile device 114. The mobile
device 114 may include one or more detectors or sensors as a positioning system built
or embedded into or within the interior of the mobile device 114. Alternatively, the
mobile device 114 uses communications signals for position determination. The mobile
device 114 may receive location data from a positioning system, such as a global positioning
system (GPS), cellular tower location methods, access point communication fingerprinting,
or the like. The server 102 may receive sensor data configured to describe a position
of a mobile device, or a controller of the mobile device 114 may receive the sensor
data from the positioning system of the mobile device 114. The mobile device 114 may
also include a system for tracking mobile device movement, such as rotation, velocity,
or acceleration. Movement information may also be determined using the positioning
system. The mobile device 114 may use the detectors and sensors to provide data indicating
a location of a vehicle. This vehicle data, also referred to herein as "probe data",
may be collected by any device capable of determining the necessary information, and
providing the necessary information to a remote entity. The mobile device 114 is one
example of a device that can function as a probe to collect probe data of a vehicle.
[0029] More specifically, probe data (e.g., collected by mobile device 114) is representative
of the location of a vehicle at a respective point in time and may be collected while
a vehicle is traveling along a route. While probe data is described herein as being
vehicle probe data, example embodiments may be implemented with pedestrian probe data,
marine vehicle probe data, or non-motorized vehicle probe data (e.g., from bicycles,
skate boards, horseback, etc.). According to the example embodiment described below
with the probe data being from motorized vehicles traveling along roadways, the probe
data may include, without limitation, location data, (e.g. a latitudinal, longitudinal
position, and/or height, GPS coordinates, proximity readings associated with a radio
frequency identification (RFID) tag, or the like), rate of travel, (e.g. speed), direction
of travel, (e.g. heading, cardinal direction, or the like), device identifier, (e.g.
vehicle identifier, user identifier, or the like), a time stamp associated with the
data collection, or the like. The mobile device 114, may be any device capable of
collecting the aforementioned probe data. Some examples of the mobile device 114 may
include specialized vehicle mapping equipment, navigational systems, mobile devices,
such as phones or personal data assistants, or the like.
[0030] An example embodiment of a processing server 102 may be embodied in an apparatus
as illustrated in FIG. 2. The apparatus, such as that shown in FIG. 2, may be specifically
configured in accordance with an example embodiment of the present invention for efficient
and effective route generation from an origin to a destination. The apparatus may
include or otherwise be in communication with a processor 202, a memory device 204,
a communication interface 206, and a user interface 208. In some embodiments, the
processor (and/or co-processors or any other processing circuitry assisting or otherwise
associated with the processor) may be in communication with the memory device via
a bus for passing information among components of the apparatus. The memory device
may be non-transitory and may include, for example, one or more volatile and/or non-volatile
memories. In other words, for example, the memory device may be an electronic storage
device (for example, a computer readable storage medium) comprising gates configured
to store data (for example, bits) that may be retrievable by a machine (for example,
a computing device like the processor 202). The memory device may be configured to
store information, data, content, applications, instructions, or the like, for enabling
the apparatus to carry out various functions in accordance with an example embodiment
of the present invention. For example, the memory device could be configured to buffer
input data for processing by the processor. Additionally or alternatively, the memory
device could be configured to store instructions for execution by the processor.
[0031] The processor 202 may be embodied in a number of different ways. For example, the
processor may be embodied as one or more of various hardware processing means such
as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP),
a processing element with or without an accompanying DSP, or various other processing
circuitry including integrated circuits such as, for example, an ASIC (application
specific integrated circuit), an FPGA (field programmable gate array), a microcontroller
unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like.
As such, in some embodiments, the processor may include one or more processing cores
configured to perform independently. A multi-core processor may enable multiprocessing
within a single physical package. Additionally or alternatively, the processor may
include one or more processors configured in tandem via the bus to enable independent
execution of instructions, pipelining and/or multithreading.
[0032] In an example embodiment, the processor 202 may be configured to execute instructions
stored in the memory device 204 or otherwise accessible to the processor. Alternatively
or additionally, the processor may be configured to execute hard coded functionality.
As such, whether configured by hardware or software methods, or by a combination thereof,
the processor may represent an entity (for example, physically embodied in circuitry)
capable of performing operations according to an embodiment of the present invention
while configured accordingly. Thus, for example, when the processor is embodied as
an ASIC, FPGA or the like, the processor may be specifically configured hardware for
conducting the operations described herein. Alternatively, as another example, when
the processor is embodied as an executor of software instructions, the instructions
may specifically configure the processor to perform the algorithms and/or operations
described herein when the instructions are executed. However, in some cases, the processor
may be a processor specific device (for example, a mobile terminal or a fixed computing
device) configured to employ an embodiment of the present invention by further configuration
of the processor by instructions for performing the algorithms and/or operations described
herein. The processor may include, among other things, a clock, an arithmetic logic
unit (ALU) and logic gates configured to support operation of the processor.
[0033] The apparatus 200 of an example embodiment may also include a communication interface
206 that may be any means such as a device or circuitry embodied in either hardware
or a combination of hardware and software that is configured to receive and/or transmit
data to/from a communications device in communication with the apparatus, such as
to facilitate communications with one or more user equipment 104 or the like. In this
regard, the communication interface may include, for example, an antenna (or multiple
antennae) and supporting hardware and/or software for enabling communications with
a wireless communication network. Additionally or alternatively, the communication
interface may include the circuitry for interacting with the antenna(s) to cause transmission
of signals via the antenna(s) or to handle receipt of signals received via the antenna(s).
In some environments, the communication interface may alternatively or also support
wired communication. As such, for example, the communication interface may include
a communication modem and/or other hardware and/or software for supporting communication
via cable, digital subscriber line (DSL), universal serial bus (USB) or other mechanisms.
[0034] The apparatus 200 may also include a user interface 208 that may, in turn be in communication
with the processor 202 to provide output to the user and, in some embodiments, to
receive an indication of a user input. As such, the user interface may include a display
and, in some embodiments, may also include a keyboard, a mouse, a joystick, a touch
screen, touch areas, soft keys, one or more microphones, a plurality of speakers,
or other input/output mechanisms. In one embodiment, the processor may comprise user
interface circuitry configured to control at least some functions of one or more user
interface elements such as a display and, in some embodiments, a plurality of speakers,
a ringer, one or more microphones and/or the like. The processor and/or user interface
circuitry comprising the processor may be configured to control one or more functions
of one or more user interface elements through computer program instructions (for
example, software and/or firmware) stored on a memory accessible to the processor
(for example, memory device 204, and/or the like).
[0035] Embodiments of the present invention provide a mechanism to derive lane-level guidance
insight from historical data and to obtain a pattern that can be used to recommend
safer ways to drive/navigate a road segment and may factor in the time of day and
weather conditions for context. Embodiments may be beneficial to both human drivers
(non-autonomous vehicles), semi-autonomous vehicles, and fully autonomous vehicles.
Personal navigation devices and in-car navigation systems may include lane-level maps;
however, they do not provide intelligent lane-level navigation information that can
advise on a relatively safe lane for a vehicle to travel in along a road segment as
the vehicle travels from an origin to a destination. Some navigation systems may provide
a mechanism to avoid lanes due to accidents, hazards, or congestion; however, this
is useful only during exceptional events. While some lane-level hazard warning may
provide a useful tool, embodiments described herein provide a comprehensive approach
to lane-level route guidance that improves safety and efficiency in a proactive manner
rather than reacting to abnormal or exceptional events on a roadway.
[0036] In traveling along a road segment, many factors influence a decision regarding which
lane a vehicle should travel in. These factors include an origin location, destination
location, traffic speeds in the different lanes, exits and entrances to the roadway,
and the like. Embodiments described herein provide smoothed lane-level guidance using
historical data crowdsourced from other drivers in order to provide a safe and efficient
path for a vehicle navigating through various road segments. Embodiments bring a new
dimension into route guidance to learn from historical behaviors regarding how drivers
have traversed the road segments in a safe manner and which lanes may be most popular
for a lane navigation sequence in traversing an origin/destination route within a
road network.
[0037] Embodiments described herein provide methods, apparatuses, and computer program products
to create historical data that represents human drivers typical (or most popular)
lane-level navigation in moving from an origin to a destination in a transportation/road
network. While vehicles may share the same road segments, they do not necessarily
share an origin or destination, even though they may be subject to similar lane-level
traffic conditions. Each driver makes lane-level decisions based on their route and
destination. Embodiments of the present disclosure create historical data that can
advise on the best or more appropriate lanes for a vehicle to navigate on a road segment.
To achieve this, data is obtained from drivers that have traversed similar origin/destination
routes so that the lane choices taken may be focused on achieving a similar journey
from the origin to the destination. Historical data is obtained that learns from how
drivers have safely driven a road at a lane-level and that data may be used to guide
new drivers traversing the same road segments. Further, embodiments may use data obtained
from drivers to inform autonomous vehicles such that autonomous vehicles may be controlled
according to the popular/safe/efficient routes selected by humans through machine
learning of the lane-level routes.
[0038] Figure 3 illustrates an example embodiment of crowdsourced data relating to lane-level
travel paths of vehicles as they traverse a road segment 250 having a traffic flow
direction illustrated by arrows 252. This data may be used for generating lane-level
maneuver pattern (LLMP) data which is the output of example embodiments described
herein. As shown, there are three vehicle paths traversing the road segment 250. Those
three paths are represented by lines 255, 260, and 265. As shown, the vehicle of path
255 begins on road segment 250 at lane four (L4) and changes lanes to lane L3 approximately
in the midpoint of road segment 250, and changes lanes again shortly thereafter to
lane L2. The vehicle of path 260 begins in lane L3, changes lanes to L2, and moves
to lane L1 before exiting road segment 250. The vehicle of path 265 begins in lane
L2 and changes lanes to lane L1 where the vehicle remains for the majority of the
road segment 250. The paths of road segment 250 of Figure 3 may be humanized lane-level
driving (HLLD) which is used to establish a lane-level maneuver pattern that depicts
the prevalent way human drivers navigate on the road segment at a lane-level when
moving from an origin to a destination. According to the embodiment of Figure 3, the
paths gravitate toward the lanes on the inner side of the curve, lanes L1 and L2.
This suggests that it is more desirable to be in lanes L1 or L2, such that a lane-level
maneuver pattern may indicate that regardless of where a vehicle enters road segment
250 (among lanes L1 to L4), it is desirable to move to an inner lane L1 or L2 before
exiting road segment 250, assuming the vehicles associated with paths 255, 260, and
265 have similar origins or destinations.
[0039] The lane-level maneuver pattern may be represented by a progression or sequence of
lanes along the road segment. Figure 4 illustrates the road segment 250 of Figure
3 depicted with sub-segment divisions of the road segment. The road segment is illustrated
as divided into four distinct road segments including the segment ending at line 310,
the segment ending at line 320, the segment ending at line 330, and the segment ending
at line 340. A lane-level maneuver pattern may be generated for a road segment or
sequence of road segments including a plurality of sub-segments for improved granularity
of the data and to avoid drastic lane change patterns across a segment, such as when
a pattern may suggest a change of three lanes across a single segment, where it is
desirable to provide such a pattern in incremental steps rather than an instruction
at the transition from one road segment to the next to move three lanes from the current
lane of travel.
[0040] According to the illustrated embodiment of Figure 4, path 255 may have a lane-level
maneuver pattern of L4 -> L4 -> L3 -> L2. The lane of the pattern may be identified
in a number of ways. For example, the lane of a sub-segment may be identified as the
lane in which the vehicle spent the majority of time or distance along the sub-segment.
Optionally, the lane may be identified as the lane in which a vehicle was when they
entered or exited a sub-segment. The lane-level maneuver pattern of path 260 may be
represented as L3 -> L2 -> L2 -> L1, while the lane-level maneuver pattern of path
265 may be represented as L2 -> L1 -> L1 -> L1.
[0041] Routes are described herein between an origin and a destination for a vehicle or
an origin and destination "pair". Vehicles may benefit from routes that do not identically
match their origin and destination, but have a similar portion of their journey. For
example, an origin and a destination may be different, but the routes may share a
common segment of limited access highway. In such an embodiment, the routes may be
a "similar" origin and destination for sharing a portion of the route that would be
traveled in the same manner (e.g., same entrance or exit to a limited access highway).
Similar origin/destination journeys reference journeys having routes that have a degree
of overlap where their overlapping route portions would be traveled in the same manner
such that lane selection along the overlapping route portions can be considered together.
Origin and destination pairs may be grouped together based on a predefined similarity
between the origin and destination pairs among a plurality of routes. This predefined
similarity may include a degree of overlap of the routes. For example, exact origins
and destinations between routes may not match; however, when the routes overlap by
a certain percentage, such as 75 percent or more, the routes may be within a predefined
similarity such that the origin and destination pairs may be considered close enough
to be used in a single grouping of lane-level maneuver patterns. Further, origins
and destinations may not be end points of a complete route, but may be end points
of a portion of a route. For example, an origin may be an entrance ramp on a limited
access highway, and a destination may be an exit ramp on the limited access highway.
In this manner, all vehicles of the same type (e.g., passenger vehicles) that enter
onto the limited access highway at the same point with the intent of exiting at the
same point may be considered as the same origin and destination pair for that portion
of their journey and should inform a lane-level maneuver pattern for such an origin
and destination pair.
[0042] Historical data obtained according to example embodiments may be categorized according
to a predefined category. These may include:
- Humanized lane-level navigation: data in this category illustrates how most human
drivers have been driving a road segment when on a similar origin/destination journey.
- Autonomous vehicle lane-level navigation: data in this category indicates how previous
autonomous vehicles have traversed such road segments at lane-level given a similar
origin and/or destination.
- General lane-level navigation: data in this category provides basic information of
how most vehicles of any kind traverse road segments given a similar origin and/or
destination.
- Link or road segments: data in this category may be based, not on similar origins
or destinations, but instead on each road segment, without regard to the origin or
destination.
[0043] Embodiments described herein inspect a data archive of vehicles that have taken similar
routes from an origin to a destination and then inspects the lane maneuver choices
of those vehicles along the route so as to inform the most prevalent lane choice.
After doing this for many routes, the data is analyzed and processed for a road segment
using data from a plurality of routes that incorporate that road segment.
[0044] A navigation pattern may be established that is historically safe, historically optimal,
or both, involving the most appropriate lane for a vehicle as it traverses the road
segments of a route. Defined herein is a method for generating an optimal and safe
navigation pattern at a lane-level. This may be achieved by obtaining historical trajectories
of vehicle journeys from origins to destinations. This may be archived data from weeks,
months, or even years. The amount of time in the historical data may be dependent
upon the frequency with which an origin-destination journey is traveled, for example.
Vehicles with similar origins and destinations or similar origin regions and similar
destination regions may be grouped, and their lane-level maneuvers may be inspected.
Vehicles may optionally be grouped by type, also. For example, large vehicles including
trucks, recreational vehicles, and the like may use different lanes than smaller vehicles
such as cars. As such, the lane-level maneuvers may be vehicle-type specific.
[0045] According to some embodiments, a link-level map matcher may be applied to probe data
trajectories from vehicles in order to obtain probe path links in sequence. This provides
a route from an origin to a destination for the respective vehicles. A subset of contiguous
links on the routes that are common to vehicles are grouped per origin and destination
of the subset, providing a broader database of origins and destinations that actual
address locations of origins and destinations where specific vehicles start and end
their respective routes. A lane-level map matcher may be run on each trajectory or
vehicle path traveled in order to obtain the lane each vehicle traveled in along their
route. This provides a path of the respective vehicles within the plurality of lanes
along road segments of the routes having a plurality of lanes. A distance metric may
be used that separates each trajectory, where the distance metric is a function of
lane center distances from a centerline of the road segment and may be a measure from
a road segment centerline to a vehicle path, thus identifying the lane of the vehicle.
The distance metric may be used in a K-medoid clustering algorithm to obtain K clusters
or most popular sequence of vehicle maneuver strategies along the road segments. The
distance metric may be a summation of lane number difference over total links on the
path of the vehicle. The medoid (center) of the clusters may represent the most popular
lane maneuver as represented by the center of the cluster for each cluster.
[0046] Once the lane-level maneuvers for a group of vehicles having a similar origin and
destination, and the lane-level maneuvers are clustered, the average time of travel
for each medoid can be compared to indicate the fastest, most efficient lane-level
maneuver strategy, while the cluster with the largest number of vehicles will represent
the most popular approach. Filtering or comparing the data against historical incident
data, particularly incident data with weather condition context, can help to obtain
relatively safe lane-level maneuvers drivers take for these roads and at specific
weather conditions. The driving speed, acceleration, and deceleration on a per-lane
basis of the medoid route (or lane maneuver trajectory) may be used to recommend driving
speeds for human drivers or speed limit for autonomous and semi-autonomous vehicles.
[0047] An example embodiment of a lane-level maneuver table depicting a lane-level maneuver
pattern artifact is illustrated below including a plurality of columns of data. The
first column, Segment-ID, refers to the identification of the road segment or link.
The time-epoch column references a time or window of time in which the lane-level
maneuver pattern is applicable, such as during a specific time of day (e.g., rush
hour, night, day, etc.). A similar column may be present including a context, such
as a weather condition which may define when the lane-level maneuver pattern is applicable.
The upstream link and downstream link columns reference road segments that are used
to enter (upstream) and exit (downstream) the road segment of the row in the table.
The maneuver column defines which lane is recommended for the road segment. The maneuver
can be a highly detailed data field including where a lane change is recommended,
how quickly a lane change from one lane to another is recommended, or a progression
of lane changes that may be recommended over the span of the road segment. The speed
column may provide further information recommended for traversing the road segment.
The speeds may also be defined by a distance along the road segment and a lane of
the road segment.
Table 1: Lane-level maneuver pattern artifact
Segment-ID |
Time-epoch |
Upstream Link |
Downstream Link |
Maneuver |
Speed (or TT) |
Unique road segment ID or Linear or Strand or SCAR |
timestamp |
Routes that uses this link to enter the road Segment |
Routesthat exists the road segment with this Link ID |
{LaneX:Distance1, LaneY:Distance2, ... LaneZ:DistanceN} |
{Distance1:Speed1, Distance2:Speed2, ... DistanceN:SpeedN} |
" |
" |
" |
" |
" |
" |
[0048] Lane changes may not be possible or safe at the position along a road segment that
the lane change is recommended. As such, the maneuver data may have a window of time
or distance along the road segment within which it is recommended to change lanes.
This may provide some degree of variability in the lane change recommendations so
a driver or autonomous vehicle does not need to attempt to change lanes as soon as
it is recommended. This further enhances the safety of the present invention by avoiding
sudden or unsafe lane changes.
[0049] According to the above-described lane-level maneuver pattern artifact of the table,
all sub-segments of the segments in the artifact should have an equal total number
of lanes. Hence, the map or road should be segmented such that a new segment is created
when a total number of lanes change. This ensures consistency among the maneuvers
of the artifact and conveys lane availability on a per segment basis.
[0050] Table 1 above may represent a the fastest, most efficient lane-level maneuver strategy
or most popular lane-level maneuver strategy for a series of road segments or road
sub-segments. However, probe data may also be collected using the same data fields
of Table 1. Thus, Table 1 may represent the collected probe data for a route from
among a plurality of probes having traveled the route and their resultant lane-level
maneuver pattern. In such an embodiment, the raw data gathered in the table may be
used to generate a table that includes the fastest, most efficient, and/or most popular
lane-level maneuver strategy for a series of road segments of a route.
[0051] Figure 5 illustrates a flowchart depicting a method according to example embodiments
of the present invention. It will be understood that each block of the flowchart and
combination of blocks in the flowchart may be implemented by various means, such as
hardware, firmware, processor, circuitry, and/or other communication devices associated
with execution of software including one or more computer program instructions. For
example, one or more of the procedures described above may be embodied by computer
program instructions. In this regard, the computer program instructions which embody
the procedures described above may be stored by a memory device 204 of an apparatus
employing an embodiment of the present invention and executed by a processor 202 of
the apparatus. As will be appreciated, any such computer program instructions may
be loaded onto a computer or other programmable apparatus (for example, hardware)
to produce a machine, such that the resulting computer or other programmable apparatus
implements the functions specified in the flowchart blocks. These computer program
instructions may also be stored in a computer-readable memory that may direct a computer
or other programmable apparatus to function in a particular manner, such that the
instructions stored in the computer-readable memory produce an article of manufacture
the execution of which implements the function specified in the flowchart blocks.
The computer program instructions may also be loaded onto a computer or other programmable
apparatus to cause a series of operations to be performed on the computer or other
programmable apparatus to produce a computer-implemented process such that the instructions
that execute on the computer or other programmable apparatus provide operations for
implementing the functions specified in the flowchart blocks.
[0052] Accordingly, blocks of the flowcharts support combinations of means for performing
the specified functions and combinations of operations for performing the specified
functions for performing the specified functions. It will also be understood that
one or more blocks of the flowcharts, and combinations of blocks in the flowcharts,
can be implemented by special purpose hardware-based computer systems that perform
the specified functions, or combinations of special purpose hardware and computer
instructions.
[0053] Figure 5 illustrates a flowchart of a method according to an example embodiment of
the present invention for establishing recommended lane-level guidance between an
origin and a destination based on a safe, efficient, or popular path. At 510, a plurality
of probe data points are received from a plurality of probe apparatuses traveling
between origins and destinations. The probe data points are map-matched, as shown
at 520, using a lane-level map matching process and system which correlates each probe
data point with an individual lane along a road segment. A lane-level maneuver pattern
is determined for each probe apparatus between the respective origin and destination
as shown at 530. The lane-level maneuver patterns for each probe apparatus are stored
in a memory at 540. Lane-level maneuver patterns are grouped together for probe apparatuses
having an origin and destination pair within a predefined similarity of origin and
destination pairs of other probe apparatuses at 550. A lane-level maneuver pattern
for each group is generated at 560 based on at least one of a popularity, efficiency,
or relatively safe lane-level maneuver pattern for the respective group. At 570, route
guidance is provided to a vehicle based on the generated lane-level maneuver pattern.
[0054] In an example embodiment, an apparatus for performing the method of Figure 5 above
may comprise a processor (e.g., the processor 202) configured to perform some or each
of the operations (510-570) described above. The processor may, for example, be configured
to perform the operations (510-570) by performing hardware implemented logical functions,
executing stored instructions, or executing algorithms for performing each of the
operations. Alternatively, the apparatus may comprise means for performing each of
the operations described above. In this regard, according to an example embodiment,
examples of means for performing operations 510-570 may comprise, for example, the
processor 202 and/or a device or circuit for executing instructions or executing an
algorithm for processing information as described above.
[0055] Many modifications and other embodiments of the inventions set forth herein will
come to mind to one skilled in the art to which these inventions pertain having the
benefit of the teachings presented in the foregoing descriptions and the associated
drawings. Therefore, it is to be understood that the inventions are not to be limited
to the specific embodiments disclosed and that modifications and other embodiments
are intended to be included within the scope of the appended claims. Moreover, although
the foregoing descriptions and the associated drawings describe example embodiments
in the context of certain example combinations of elements and/or functions, it should
be appreciated that different combinations of elements and/or functions may be provided
by alternative embodiments without departing from the scope of the appended claims.
In this regard, for example, different combinations of elements and/or functions than
those explicitly described above are also contemplated as may be set forth in some
of the appended claims. Although specific terms are employed herein, they are used
in a generic and descriptive sense only and not for purposes of limitation.
1. A mapping system comprising:
a memory comprising map data; and
processing circuitry configured to:
receive a plurality of probe data points, each probe data point received from a probe
apparatus of a plurality of probe apparatuses, each probe apparatus traveling between
a respective origin and destination pair, each probe apparatus comprising one or more
sensors and being onboard a respective vehicle, wherein each probe data point comprises
location information associated with the respective probe apparatus;
determine a lane-level maneuver pattern for each probe apparatus between the origin
and the destination;
provide for storage of the lane-level maneuver patterns for each probe apparatus in
the memory;
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses;
generate a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group;
and
provide for route guidance to a vehicle based on the generated lane-level maneuver
pattern.
2. The mapping system of claim 1, wherein the route guidance provided to a vehicle comprises
lane-level route guidance for an autonomous vehicle along a route between an origin
and a destination.
3. The mapping system of claim 1, wherein the processing circuitry configured to determine
a lane-level maneuver pattern for each probe apparatus between the origin and the
destination comprises processing circuitry configured to:
map match probe data from a respective probe apparatus to one or more road segments
along a route between the origin and the destination;
map match probe data from the respective probe apparatus to individual lanes of the
one or more road segments along the route between the origin and the destination;
and
determine a lane-level maneuver pattern for the respective probe apparatus based on
map matched lanes of the one or more road segments in a sequence from the origin to
the destination.
4. The mapping system of claim 1, wherein the processing circuitry configured to generate
a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group
comprises processing circuitry configured to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a largest number of associated probe apparatuses; and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the largest number of associated probe apparatuses.
5. The mapping system of claim 1, wherein the processing circuitry configured to generate
a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group
comprises processing circuitry configured to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a shortest time difference between the origin and the destination;
and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the shortest time difference between the origin
and the destination
6. The mapping system of claim 1, wherein the processing circuitry configured to group
together lane-level maneuver patterns for probe apparatuses having an origin and destination
pair within a predefined similarity of origin and destination pairs of other probe
apparatuses further comprises processing circuitry configured to:
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses and having traveled between the origin and the destination
within a predetermined epoch;
wherein the processing circuitry configured to generate a lane-level maneuver pattern
for each group based on at least one of a popularity, efficiency, or relatively safe
lane-level maneuver pattern for the respective group further comprises processing
circuitry to generate a lane-level maneuver pattern based, at least in part, on an
epoch in which the route between the origin and the destination will be traveled.
7. The mapping system of claim 1, wherein the processing circuitry is further configured
to:
provide driving speed, acceleration, and deceleration on a per-lane basis of the lane-level
maneuver pattern based, at least in part, on the group of probe apparatuses associated
with the lane-level maneuver pattern.
8. An apparatus comprising at least one processor and at least one memory including computer
program code, the at least one memory and computer program code configured to, with
the processor, cause the apparatus to at least:
receive a plurality of probe data points, each probe data point received from a probe
apparatus of a plurality of probe apparatuses, each probe apparatus traveling between
a respective origin and destination pair, each probe apparatus comprising one or more
sensors and being onboard a respective vehicle, wherein each probe data point comprises
location information associated with the respective probe apparatus;
determine a lane-level maneuver pattern for each probe apparatus between the origin
and the destination;
provide for storage of the lane-level maneuver patterns for each probe apparatus in
the memory;
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses;
generate a lane-level maneuver pattern for each group based on at least one of a popularity,
efficiency, or relatively safe lane-level maneuver pattern for the respective group;
and
provide for route guidance to a vehicle based on the generated lane-level maneuver
pattern.
9. The apparatus of claim 8, wherein the route guidance provided to a vehicle comprises
lane-level route guidance for an autonomous vehicle along a route between an origin
and a destination.
10. The apparatus of claim 8, wherein causing the apparatus to determine a lane-level
maneuver pattern for each probe apparatus between the origin and the destination comprises
causing the apparatus to:
map match probe data from a respective probe apparatus to one or more road segments
along a route between the origin and the destination;
map match probe data from the respective probe apparatus to individual lanes of the
one or more road segments along the route between the origin and the destination;
and
determine a lane-level maneuver pattern for the respective probe apparatus based on
map matched lanes of the one or more road segments in a sequence from the origin to
the destination.
11. The apparatus of claim 8, wherein causing the apparatus to generate a lane-level maneuver
pattern for each group based on at least one of a popularity, efficiency, or relatively
safe lane-level maneuver pattern for the respective group comprises causing the apparatus
to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a largest number of associated probe apparatuses; and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the largest number of associated probe apparatuses.
12. The apparatus of claim 8, wherein causing the apparatus to generate a lane-level maneuver
pattern for each group based on at least one of a popularity, efficiency, or relatively
safe lane-level maneuver pattern for the respective group comprises causing the apparatus
to:
apply a clustering algorithm to each group to obtain clusters of lane-level maneuver
patterns within the respective group;
select the cluster having a shortest time difference between the origin and the destination;
and
generate the lane-level maneuver pattern for the respective group based on the lane-level
maneuver pattern of the cluster having the shortest time difference between the origin
and the destination.
13. The apparatus of claim 8, wherein causing the apparatus to group together lane-level
maneuver patterns for probe apparatuses having an origin and destination pair within
a predefined similarity of origin and destination pairs of other probe apparatuses
further comprises causing the apparatus to:
group together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses and having traveled between the origin and the destination
within a predetermined epoch;
wherein causing the apparatus to generate a lane-level maneuver pattern for each group
based on at least one of a popularity, efficiency, or relatively safe lane-level maneuver
pattern for the respective group further comprises causing the apparatus to generate
a lane-level maneuver pattern based, at least in part, on an epoch in which the route
between the origin and the destination will be traveled.
14. The apparatus of claim 8, wherein the apparatus is further caused to:
provide driving speed, acceleration, and deceleration on a per-lane basis of the lane-level
maneuver pattern based, at least in part, on the group of probe apparatuses associated
with the lane-level maneuver pattern.
15. A method comprising:
receiving a plurality of probe data points, each probe data point received from a
probe apparatus of a plurality of probe apparatuses, each probe apparatus traveling
between a respective origin and destination pair, each probe apparatus comprising
one or more sensors and being onboard a respective vehicle, wherein each probe data
point comprises location information associated with the respective probe apparatus;
determining a lane-level maneuver pattern for each probe apparatus between the origin
and the destination;
providing for storage of the lane-level maneuver patterns for each probe apparatus
in a memory;
grouping together lane-level maneuver patterns for probe apparatuses having an origin
and destination pair within a predefined similarity of origin and destination pairs
of other probe apparatuses;
generating a lane-level maneuver pattern for each group based on at least one of a
popularity, efficiency, or relatively safe lane-level maneuver pattern for the respective
group; and
providing for route guidance to a vehicle based on the generated lane-level maneuver
pattern.